Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
theme_set(theme_minimal())
library(dplyr)

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Put your homework on GitHub!

If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 4th weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab under Stage and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)

Starbucks = rename(Starbucks, own_type = `Ownership Type`)

# Plot the points on the map
ggmap(world) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = own_type),
             alpha = .3, 
             size = .1)  +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Global Starbucks Locations by Ownership Type")

There seem to be a lot of company owned and franchise locations in the US, but in places like Japan they are all joint venture. There are also more Starbucks in the U.S. than other locations, but a lot in Asia as well.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

twin_cities <- get_stamenmap(
    bbox = c(left = -93.4460, bottom = 44.8019, right = -92.7278, top = 45.1351), 
    maptype = "terrain",
    zoom = 11)

# Plot the points on the map
ggmap(twin_cities) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             alpha = 1, 
             size = 2) +
  theme_map() +
  labs(title = "Starbucks locations in the Twin Cities metro area")

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

It tells me this may take a while and to try a smaller zoom. I wasn’t even able to load the map with a zoom up to 14, and although I could load the map at a zoom of 13 it took a long time. When I used a zoom of 8 it loaded very quickly but not enough tiles were used and the map was blurry. Zooming in loads more tiles and more detail but takes a long time.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

twin_cities <- get_stamenmap(
    bbox = c(left = -93.4460, bottom = 44.8019, right = -92.7278, top = 45.1351), 
    maptype = "toner",
    zoom = 11)

# Plot the points on the map
ggmap(twin_cities) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             alpha = 1, 
             size = 2) +
  theme_map() +
  labs(title = "Starbucks locations in the Twin Cities metro area")

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

twin_cities <- get_stamenmap(
    bbox = c(left = -93.4460, bottom = 44.8019, right = -92.7278, top = 45.1351), 
    maptype = "terrain",
    zoom = 11)

# Plot the points on the map
ggmap(twin_cities) + # creates the map "background"
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             alpha = 1, 
             size = 2) +
  theme_map() +
  annotate("point", x = -93.16, y = 44.93, 
           color="orange", size=2) +
  geom_label(aes( x=-93.11, y=44.92, label= 'Macalester College'),                 , 
           color="orange", 
           size=2 , angle=45, fontface="bold" ) +
   labs(title = "Starbucks locations in the Twin Cities metro area")

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

Line 188 reads in the data from a link online and names the dataset. Line 189 changes the formatting of the state variable and separates it into two separate variables. Line 190 gets rid of the dot variable. Line 191 changes the states variable to be all lowercase. Lines 193 and 194 create a new dataset called starbucks_with_2018_pop_est, made up of the starbucks_us_by_state from before. Then Line 194 adds the census_pop_est_2018 to the starbucks_us_by_state through the join function. Line 196 tells it to joint the two datasets with the state variable, but in one dataset it is called state_name so you have to tell it that state_name is the same thing as state. Linke 197 creates a new variable that gives the number of Starbucks per 10,000 people and adds this variable to the starbucks_with_2018_pop_est dataset.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

states_map <- map_data("state")

new_Starbucks <- Starbucks %>% 
  left_join(starbucks_with_2018_pop_est, 
            by = "State/Province") %>% 
  filter(Country == "US") %>% 
  filter(`State/Province` != "Alaska") %>% 
  filter(`State/Province` != "Hawaii")

new_Starbucks %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  scale_fill_viridis_c(option = "plasma") +
  geom_point(aes(x = Longitude, y = Latitude), 
             alpha = .2, 
             size = .05) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  labs(title = "Number of Starbucks per 10,000 people in the US", caption = "Created by: Maya Hajny Fernandez")

This plot shows that there are more Starbucks in proportion to the population on the west coast overall, and that most Starbucks are concentrated in cities. Especially in Washington State, which is the birhtplace of the company, there are many starbucks compared to the population.

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

favorite_places_mhf <- tibble(
  place = c("Grandparents House in Sunriver, OR", 
            "Forest Park in PDX", 
            "Haystack Rock", "Sauvie Island", 
            "Timberline Lodge", "Alameda, CA", 
            "Palacio Real in Madrid, Spain", 
            "PDX Home", "Solstice Pizza", 
            "Macalester College", 
            "Mississippi River Overlook", 
            "Trader Joe's", "St. Paul Home", 
            "Playa El Laucho in Arica, Chile"),
  long = c(-121.42, -122.72, -123.964, -122.79, 
           -121.710, -122.2651, -3.71, -122.77, 
           -121.51747, -93.199, -93.1712321, -93.145, 
           -93.167, -70.33),
  lat = c(43.90, 45.536, 45.884, 45.808, 45.331, 
          37.759, 40.418, 45.46, 45.714, 44.9378965,
          44.94, 44.926, 44.93, -18.487),
  top_three = c("Yes", "No", "No", "No", "No", 
                "No", "No", "No", "No", "Yes", 
                "Yes", "No", "No", "No")
)

The data are ordered and connected chronologically, from place I have visited first in my life to the last place I have visited. This was important to me when coming up with the the points that I wanted to include.

pal_3_fav <- colorFactor("viridis", 
                    domain = favorite_places_mhf$top_three) 


leaflet(data = favorite_places_mhf) %>% 
  addProviderTiles(providers$Esri.NatGeoWorldMap) %>% 
  addCircles(lng = ~long, 
             lat = ~lat, 
             label = ~place,
             opacity = 10, 
             color = ~pal_3_fav(top_three)) %>% 
  addPolylines(lng = ~long,
               lat = ~lat,
               color = col2hex("darkblue")) %>% 
      addLegend(pal = pal_3_fav, 
            values = ~top_three,
            position = "bottomright")

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

US_map <- get_stamenmap(
     bbox = c(left = -77.1604, bottom = 38.7907, right = -76.9213, top = 39.0048), #play with these dimensions
     maptype = "terrain",
     zoom = 12)

Station_Trips <- Trips %>% 
  mutate(name = sstation) %>% 
  left_join(Stations,
           by = c("name")) %>% 
  group_by(lat, long) %>% 
  summarize(freq_stations = n())
  
ggmap(US_map) + 
     geom_point(data = Station_Trips, 
             aes(x = long, y = lat, color = freq_stations), 
             alpha = 1, 
             size = 1) +
 theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Total Number of Departures from Each Station")

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

US_map <- get_stamenmap(
     bbox = c(left = -77.1604, bottom = 38.7907, right = -76.9213, top = 39.0048),
     maptype = "terrain",
     zoom = 12)

Station_Trips <- Trips %>% 
  mutate(name = sstation) %>% 
  left_join(Stations,
           by = c("name")) %>% 
  mutate(cas_true = client == "Casual") %>% 
  group_by(lat, long) %>% 
  summarize(pct_cas = sum(cas_true)/n()) 
  
ggmap(US_map) + 
     geom_point(data = Station_Trips, 
             aes(x = long, y = lat, color = pct_cas), 
             alpha = 1, 
             size = 1) +
 theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Departures from Each Station by Percent of Casual Users")

A higher percentage of casual users are located closer to the center of the city, while most of the users on the outskirts have a higher percentage of registered users. This makes sense in terms of tourism.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

states_map <- map_data("state")

covid19 %>% 
  group_by(state) %>% 
  summarize(sum_covid = max(cases)) %>% 
  mutate(new_state = str_to_lower(state)) %>% 
  ggplot(aes(fill = sum_covid)) +
  geom_map(map = states_map,
           aes(map_id = new_state)) +
  scale_fill_distiller(palette = "YlGnBu", 
                       direction = 1,
                       labels = scales::comma_format()) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  labs(title = "The Most Recent Cumulative Number of COVID-19 Cases")

The greatest number of covid-19 cases occurred in states that have the highest populations, which makes sense because the more people in a population, the greater number of cases. This map doesn’t give an indication of population, however, and I only knew this because of previous knowledge, not from any information provided by the map.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid_with_10000 <-
  covid19 %>% 
  group_by(state) %>% 
  summarize(sum_covid = max(cases)) %>% 
  mutate(new_state = str_to_lower(state)) %>% 
  left_join(census_pop_est_2018, by = c("new_state" = "state")) %>% 
  mutate(cases_per_ten = (sum_covid/est_pop_2018)*10000)

states_map <- map_data("state")

covid_with_10000 %>% 
  ggplot(aes(fill = cases_per_ten)) +
  geom_map(map = states_map,
           aes(map_id = new_state)) +
  scale_fill_distiller(palette = "YlGnBu", 
                       direction = 1,
                       labels = scales::comma_format()) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map()  +
  labs(title = "Most Recent Cumulative Cases per 10,000 people")

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

data("MplsStops")
data("MplsDemo")

mpls_suspicious <- MplsStops %>%
 group_by(neighborhood) %>% 
summarize(freq_stops_neigh = n(), 
          prop_suspicious = mean(problem == "suspicious")) %>% 
  arrange(freq_stops_neigh,
          .desc = TRUE)

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal4 <- colorFactor("inferno", 
                     domain = MplsStops$problem)

leaflet(MplsStops) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~long, 
            lat = ~lat, 
            stroke = FALSE,
            radius = 2,
             weight = 10, 
             opacity = 1,
            fillColor = ~pal4(problem)) %>% 
  addLegend(position = "bottomright",
            pal = pal4, 
            values = ~problem,
            title = "Type of Stop")

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

mpls_all <- mpls_nbhd %>% 
  left_join(mpls_suspicious, by = c("BDNAME" = "neighborhood")) %>% 
  left_join(MplsDemo, by = c("BDNAME" = "neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

pal5 <- colorFactor("inferno", 
                     domain = mpls_all$prop_suspicious)

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addPolygons(stroke = FALSE,
              fillOpacity = .7,
              highlight = highlightOptions( 
                                 color = "black",
                                 fillOpacity = 0.9,
                                 bringToFront = FALSE),
            fillColor = ~pal5(prop_suspicious),
            label = ~str_to_title(BDNAME)) 

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

pal6 <- colorFactor("magma", 
                     domain = mpls_all$poverty)

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addPolygons(stroke = FALSE,
              fillOpacity = .7,
              highlight = highlightOptions( 
                                 color = "black",
                                 fillOpacity = 0.9,
                                 bringToFront = FALSE),
            fillColor = ~pal6(poverty),
            label = ~str_to_title(BDNAME))

I wondered how poverty varied by neighborhood in Minneapolis. There don’t seem to be huge trends in the data, but when you compare this graph to the one above that indicates the proportion of suspicious stops, there seems to be an inverse relationship in some neighborhoods. Areas like Como and the Minneapolis lakes area seem to have lower poverty rates, which makes sense based on my previous knowledge. The neighborhoods with lower poverty levels seem to be more intertwined with the neighborhoods that have higher poverty levels.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?